arXiv subjects

Lingling Yang

Publications and source records attributed to Lingling Yang.

5 recordsLinked to original sources

arXiv2025

Deciphering the lattice vibrational behaviors of CuInP2S6 by angle-resolved polarized Raman scattering

The layered van der Waals (vdW) ferroelectric CuInP2S6 (CIPS) exhibits unique cation hopping-driven phenomena that bring about unconventional properties with intriguing mechanisms and hold promises for advanced applications in nanoelectronics. However, an explicit analysis of its lattice dynamics and vibrational symmetries, pivotal for understanding the material's peculiar ferroelectric and ferroionic behaviors, remains incomplete. Here, we employ angle-resolved polarized Raman spectroscopy in concert with first-principles calculations to systematically unravel the anisotropic lattice vibrations of CIPS single crystals. By analyzing the polarization-dependent Raman intensities, we determine the symmetry assignments and Raman tensors of all major vibrational modes, revealing good agreement with theoretical predictions. Furthermore, we demonstrate the utility of Raman spectroscopy as a sensitive and non-invasive probe for structural and ferroelectric order evolution, by examining temperature-driven phase transitions and thickness-dependent polarization suppression in CIPS. Our findings establish a foundational framework for correlating lattice dynamics with functional properties in CIPS and provide a methodological blueprint for studying other vdW ferroelectrics.

cond-mat.mtrl-sci↗

arXiv2023

Bogdanov-Takens bifurcation of codimension $3$ in the Gierer-Meinhardt model

Bifurcation of the local Gierer-Meinhardt model is analyzed in this paper. It is found that the degenerate Bogdanov-Takens bifurcation of codimension 3 happens in the model, except that teh saddle-node bifurcation and the Hopf bifurcation. That was not reported in the existing results about this model. The existence of equilibria, their stability, the bifurcation and the induced complicated and interesting dynamics are explored in detail, by using the stability analysis, the normal form method and bifurcation theory. Numerical results are also presented to validate theoretical results.

math.DS↗

arXiv2019

Decoding of visual-related information from the human EEG using an end-to-end deep learning approach

There is increasing interest in using deep learning approach for EEG analysis as there are still rooms for the improvement of EEG analysis in its accuracy. Convolutional long short-term (CNNLSTM) has been successfully applied in time series data with spatial structure through end-to-end learning. Here, we proposed a CNNLSTM based neural network architecture termed EEG_CNNLSTMNet for the classification of EEG signals in response to grating stimuli with different spatial frequencies. EEG_CNNLSTMNet comprises two convolutional layers and one bidirectional long short-term memory (LSTM) layer. The convolutional layers capture local temporal characteristics of the EEG signal at each channel as well as global spatial characteristics across channels, while the LSTM layer extracts long-term temporal dependency of EEG signals. Our experiment showed that EEG_CNNLSTMNet performed much better at EEG classification than a traditional machine learning approach, i.e. a support vector machine (SVM) with features. Additionally, EEG_CNNLSTMNet outperformed EEGNet, a state-of-art neural network architecture for the intra-subject case. We infer that the underperformance when using an LSTM layer in the inter-subject case is due to long-term dependency characteristics in the EEG signal that vary greatly across subjects. Moreover, the inter-subject fine-tuned classification model using very little data of the new subject achieved much higher accuracy than that trained only on the data from the other subjects. Our study suggests that the fine-tuned inter-subject model can be a potential end-to-end EEG analysis method considering both the accuracy and the required training data of the new subject.

eess.SP↗

arXiv2019

Prediction Modeling and Analysis for Telecom Customer Churn in Two Months

A practical churn customer prediction model is critical to retain customers for telecom companies in the saturated and competitive market. Previous studies focus on predicting churn customers in current or next month, in which telecom companies don't have enough time to develop and carry out churn management strategies. In this paper, we propose a new T+2 churn customer prediction model, in which the churn customers in two months are recognized and the one-month window T+1 is reserved to carry out churn management strategies. However, the predictions for churn customers in two months are much more difficult than in current or next month because of the weaker correlation between the customer information and churn states. Two characteristics of telecom dataset, the discrimination between churn and non-churn customers is complicated and the class imbalance problem is serious, are observed. To discriminate the churn customers accurately, random forest (RF) classifier is chosen because RF solves the nonlinear separable problem with low bias and low variance and handles high feature spaces and large number of training examples. To overcome the imbalance problem, synthetic minority over-sampling with borderline or tomek link, in which the distribution of the samples remains and the number of the training examples becomes larger, is applied. Overall, a precision ratio of about 50% with a recall ratio of about 50% is achieved in the T+2 churn prediction. The proposed prediction model provides an accurate and operable churn customer prediction model for telecom companies.

cs.IR↗

arXiv2016

Carbon Decorated TiO2 Nanotube Membranes: A Renewable Nanofilter for Size- and Charge Selective Enrichment of Proteins

In this work, we design a TiO2 nanomembrane (TiNM) that can be used as a nanofilter platform for a selective enrichment of specific proteins. After use the photocatalytic properties of TiO2 allow to decompose unwanted remnant on the substrate and thus make the platform reusable. To construct this platform we fabricate a free-standing TiO2 nanotube array and remove the bottom oxide to form a both-end open TiNM. By pyrolysis of the natural tube wall contamination (C/TiNM), the walls become decorated with graphitic carbon patches. Owing to the large surface area, the amphiphilic nature and the charge adjustable character, this C/TiNM can be used to extract and enrich hydrophobic and charged biomolecules from solutions. Using human serum albumin (HSA) as a model protein as well as protein mixtures, we show that the composite membrane exhibits a highly enhanced loading capacity and protein selectivity and is reusable after a short UV treatment.

cond-mat.mtrl-sci↗